DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks
作者：
Highlights：
• We propose two distributed label propagation algorithms for heterogeneous networks.
• The scalability of algorithms is measured on a heterogeneous drug-related network.
• We evaluate the effectiveness of the algorithms for “drug repositioning".
• The runtime of the algorithms is dramatically decreased.
• Experiments indicate the high accuracy of our proposed algorithms.
摘要
•We propose two distributed label propagation algorithms for heterogeneous networks.•The scalability of algorithms is measured on a heterogeneous drug-related network.•We evaluate the effectiveness of the algorithms for “drug repositioning".•The runtime of the algorithms is dramatically decreased.•Experiments indicate the high accuracy of our proposed algorithms.
论文关键词：Distributed graph processing,Heterogeneous label propagation,Complex networks,Semi-supervised learning,Drug repositioning,Apache Giraph
论文评审过程：Received 13 November 2019, Revised 7 February 2020, Accepted 4 June 2020, Available online 9 June 2020, Version of Record 25 June 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113640
